Covariance Based Deep Feature for Text-Dependent Speaker Verification

d-vector approach achieved impressive results in speaker verification. Representation is obtained at utterance level by calculating the mean of the frame level outputs of a hidden layer of the DNN. Although mean based speaker identity representation has achieved good performance, it ignores the variability of frames across the whole utterance, which consequently leads to information loss. This is particularly serious for text-dependent speaker verification, where within-utterance feature variability better reflects text variability than the mean. To address this issue, a new covariance based speaker representation is proposed in this paper. Here, covariance of the frame level outputs is calculated and incorporated into the speaker identity representation. The proposed approach is investigated within a joint multi-task learning framework for text-dependent speaker verification. Experiments on RSR2015 and RedDots showed that, covariance based deep feature can significantly improve the performance compared to the traditional mean based deep features.